R: Преобразование широкой таблицы в узкую с помощью столбцов Specifi c - PullRequest
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R: Преобразование широкой таблицы в узкую с помощью столбцов Specifi c

0 голосов
/ 09 февраля 2020

У меня есть широкая таблица с 161 переменной, которую я хотел бы преобразовать в узкую таблицу только с 13 переменными. Насколько я могу судить, я не могу использовать pivot_longer, потому что мне нужно ввести дополнительный столбец для года, а затем выбрать только определенные столбцы из широкой таблицы для каждой строки.

Я начал с пустого фрейма данных: 

subset_gathered <- read.csv(text = "FIPS_State, Place_Code, Place_Name, Longitude, Latitude, County, Closest Metro, Year, Population_Density, Labor Force, Employed, Unemployed, Unemployment Rate", 
                            colClasses = c("character", "integer", "character", "numeric", "numeric", "character", "numeric","integer", "numeric", "integer", "integer", "integer", "numeric") )

, а затем попытался для l oop выбрать каждый из соответствующих столбцов (фактическое решение будет повторяться в течение 1990- 2018, где каждая переменная представляет собой отдельный столбец) 

for(i in subset[1:5,]){
  temp1990 <- c(subset$`FIPS State`[i], subset$`Place Code`[i], subset$Name[i], subset$longitude[i], subset$latitude[i], subset$COUNTY[i], subset$closest_metro[i], 1990, subset$density1990[i], subset$`Labor Force.90`[i], subset$Employed.90[i], subset$`Unemployment Level.90`[i], subset$`Unemployment Rate.90`[i])
  subset_gathered[nrow(subset_gathered)+1,]<- temp1990
  temp1991 <- c(subset$`FIPS State`[i], subset$`Place Code`[i], subset$Name[i], subset$longitude[i], subset$latitude[i], subset$COUNTY[i], subset$closest_metro[i], 1991, subset$density1991[i], subset$`Labor Force.91`[i], subset$Employed.91[i], subset$`Unemployment Level.91`[i], subset$`Unemployment Rate.91`[i])
  subset_gathered[nrow(subset_gathered)+1,]<- temp1991
}

Я предполагаю, что результаты странны, потому что он запускает каждый вектор за раз, а не строку 

head(subset_gathered)
  FIPS_State Place_Code Place_Name Longitude Latitude County Closest.Metro Year Population_Density Labor.Force
1          1          1          1         1        1    124           124  124                124         124
2          1          1          1         1        1    124           124  124                124         124
3       <NA>       <NA>       <NA>      <NA>     <NA>   <NA>          <NA> <NA>               <NA>        <NA>
4       <NA>       <NA>       <NA>      <NA>     <NA>   <NA>          <NA> <NA>               <NA>        <NA>
5       <NA>       <NA>       <NA>      <NA>     <NA>   <NA>          <NA> <NA>               <NA>        <NA>
6       <NA>       <NA>       <NA>      <NA>     <NA>   <NA>          <NA> <NA>               <NA>        <NA>
        Employed     Unemployed Unemployment.Rate
1 Abbeville city Abbeville city    Abbeville city
2 Abbeville city Abbeville city    Abbeville city
3           <NA>           <NA>              <NA>
4           <NA>           <NA>              <NA>
5           <NA>           <NA>              <NA>
6           <NA>           <NA>              <NA>

Если я должны были выделить строки вне a для l oop, я получаю именно те результаты, которые мне нужны: 

temp1990 <- c(subset$`FIPS State`[1], subset$`Place Code`[1], subset$Name[1], subset$longitude[1], subset$latitude[1], subset$COUNTY[1], subset$closest_metro[1], 1990, subset$density1990[1], subset$`Labor Force.90`[1], subset$Employed.90[1], subset$`Unemployment Level.90`[1], subset$`Unemployment Rate.90`[1])
subset_gathered[nrow(subset_gathered)+1,]<- temp1990
temp1991 <- c(subset$`FIPS State`[1], subset$`Place Code`[1], subset$Name[1], subset$longitude[1], subset$latitude[1], subset$COUNTY[1], subset$closest_metro[1], 1991, subset$density1991[1], subset$`Labor Force.91`[1], subset$Employed.91[1], subset$`Unemployment Level.91`[1], subset$`Unemployment Rate.91`[1])
subset_gathered[nrow(subset_gathered)+1,]<- temp1991

  FIPS_State Place_Code     Place_Name Longitude  Latitude       County    Closest.Metro Year   Population_Density
1          1        124 Abbeville city  -85.2513 31.567949 Henry County 24.3361834333029 1990 7.85948198868711e-05
2          1        124 Abbeville city  -85.2513 31.567949 Henry County 24.3361834333029 1991 7.96367966656743e-05
  Labor.Force Employed Unemployed Unemployment.Rate
1        6867     6539        328               4.8
2        6648     6106        542               8.2

Затем я попытался написать функцию apply, но она вернула только одна строка, и она должна была вернуть 5 (в subset есть 5 строк с данными). 

subset_gathered[nrow(subset_gathered)+1,]<- apply(subset, 1, function(x)  c(x[1], x[2], x[3], x[37], x[38], x[41], x[40], 1990, x[7], x[118], x[119], x[120], x[121]))

  FIPS_State Place_Code     Place_Name Longitude Latitude       County Closest.Metro Year Population_Density
1          1        124 Abbeville city -85.25130 31.56795 Henry County      24.33618 1990       7.859482e-05
  Labor.Force Employed Unemployed Unemployment.Rate
1        6867     6539        328               4.8

Я на правильном пути? Я думаю, что использование для l oop будет работать в Python или SAS, но apply всегда кажется правильным ответом в R, но я все еще не могу получить нужные мне результаты.

dput: 

subset<- structure(list(`FIPS State` = c(1, 1, 1, 1, 1), `Place Code` = c(124, 
484, 676, 1228, 1396), Name = c("Abbeville city", "Addison town", 
"Akron town", "Aliceville city", "Allgood town"), `1990 Pop` = c(3168, 
639, 468, 3052, 461), GEOID = c(100124, 100484, 100676, 101228, 
101396), USPS = c("AL", "AL", "AL", "AL", "AL"), density1990 = c(7.85948198868711e-05, 
8.31165452653486e-05, 0.000325678496868476, 0.000262064228061137, 
0.000171694599627561), density1991 = c(7.96367966656743e-05, 
8.28563995837669e-05, 0.000329157967988866, 0.000260604499398935, 
0.000176163873370577), density1992 = c(7.89669544507294e-05, 
8.37669094693028e-05, 0.000331941544885177, 0.000259402369912416, 
0.00017877094972067), density1993 = c(7.89421454798055e-05, 8.49375650364204e-05, 
0.000331941544885177, 0.000256568778979907, 0.000183612662942272
), density1994 = c(7.87188647414905e-05, 8.6888657648283e-05, 
0.0003312456506611, 0.000252189592993302, 0.000187709497206704
), density1995 = c(7.77381836037938e-05, 7.39452771986497e-05, 
0.000332684436284319, 0.000248944989340191, 0.000195857866765266
), density1996 = c(7.63486725020343e-05, 7.4934144204278e-05, 
0.000334772413917902, 0.000251349425249651, 0.000203304933942652
), density1997 = c(7.62246090108058e-05, 7.54835147629603e-05, 
0.000334772413917902, 0.000249460225606504, 0.000213358474632124
), density1998 = c(7.60012947265945e-05, 7.58131370981698e-05, 
0.000338252376640541, 0.0002473134078302, 0.000222667308603857
), density1999 = c(7.51328502879948e-05, 7.66921299920616e-05, 
0.000338948369185069, 0.000244479608365479, 0.000230859082498983
), density2000 = c(7.41651550564123e-05, 7.66921299920616e-05, 
0.000339644361729597, 0.000225673484645058, 0.000246497923571494
), density2001 = c(7.29741455406185e-05, 7.7021752327271e-05, 
0.000329204473561679, 0.000225244121089797, 0.000245008510136017
), density2002 = c(7.21801391967559e-05, 7.77908711094264e-05, 
0.00031598061521565, 0.000224900630245588, 0.000243891450059409
), density2003 = c(7.15101963441219e-05, 7.84501157798452e-05, 
0.000307628704681315, 0.000221637467225606, 0.00024351909670054
), density2004 = c(7.08898788879792e-05, 7.93291086737371e-05, 
0.00029649282396887, 0.000220091758426668, 0.000242029683265062
), density2005 = c(7.00527969547999e-05, 7.47440401111995e-05, 
0.000233954174226123, 0.000214997442884744, 0.000239751372251362
), density2006 = c(6.97298585291218e-05, 7.59743946809311e-05, 
0.000227205496123447, 0.00021432028558432, 0.000239007957143606
), density2007 = c(6.91585059298451e-05, 7.68971606082299e-05, 
0.000222143987546439, 0.000212881326320918, 0.00023826454203585
), density2008 = c(6.8115166400731e-05, 7.71022197031852e-05, 
0.000215957699285652, 0.000212542747670706, 0.000237149419374215
), density2009 = c(6.7419606714655e-05, 7.78199265355286e-05, 
0.000207521851657307, 0.000211103788407304, 0.000234547466497069
), density2010 = c(6.67737298632987e-05, 7.7717396988051e-05, 
0.00020021078371274, 0.00021042663110688, 0.000231202098512166
), density2011 = c(6.65004742723402e-05, 7.69996901557075e-05, 
0.000194024495451953, 0.000205347951353697, 0.000231573806066044
), density2012 = c(6.53080846167217e-05, 7.62819207541575e-05, 
0.000194238820289117, 0.000205286788530157, 0.000231202012572632
), density2013 = c(6.48857660254055e-05, 7.61794069120457e-05, 
0.00019198677599591, 0.000202371348887106, 0.00023083030515692
), density2014 = c(6.44634620351942e-05, 7.58718184588342e-05, 
0.000190860753849307, 0.000200340867125697, 0.000231202012572632
), density2015 = c(6.42895721568719e-05, 7.53591710368151e-05, 
0.000189734731702703, 0.000199579436465168, 0.00023083030515692
), density2016 = c(6.39169509890384e-05, 7.52566415524112e-05, 
0.000186919676336194, 0.000198225781957562, 0.00023083030515692
), density2017 = c(6.37430611107161e-05, 7.46414646459882e-05, 
0.000186919781574101, 0.000197548954703759, 0.000231202012572632
), density2018 = c(6.36685368771494e-05, 7.4231339097472e-05, 
0.000185793758793534, 0.000194672438875096, 0.000231202012572632
), minpop = c(2563, 637, 330, 2301, 461), longitude = c(-85.2513, 
-87.177851, -87.738779, -88.154427, -86.516109), latitude = c(31.567949, 
34.202689, 32.879495, 33.126276, 33.907623), is_metro = c(0, 
0, 0, 0, 0), closest_metro = c(24.3361834333029, 28.0961047219205, 
27.2224172144133, 34.7564812052357, 30.296520864832), COUNTY = c("Henry County", 
"Winston County", "Hale County", "Pickens County", "Blount County"
), COUNTY2 = c(NA_character_, NA_character_, NA_character_, NA_character_, 
NA_character_), `LAUS Code` = c("CN0106700000000", "CN0113300000000", 
"CN0106500000000", "CN0110700000000", "CN0100900000000"), `State FIPS` = c("01", 
"01", "01", "01", "01"), `County FIPS` = c("067", "133", "065", 
"107", "009"), `Labor Force.00` = c(7634, 11687, 6976, 8597, 
25106), Employed.00 = c(7259, 10776, 6557, 7952, 24231), `Unemployment Level.00` = c(375, 
911, 419, 645, 875), `Unemployment Rate.00` = c(4.9, 7.8, 6, 
7.5, 3.5), `Labor Force.01` = c(7542, 10737, 7019, 8440, 25305
), Employed.01 = c(7137, 9732, 6513, 7732, 24393), `Unemployment Level.01` = c(405, 
1005, 506, 708, 912), `Unemployment Rate.01` = c(5.4, 9.4, 7.2, 
8.4, 3.6), `Labor Force.02` = c(7530, 10478, 7054, 8248, 25757
), Employed.02 = c(7075, 9561, 6462, 7569, 24366), `Unemployment Level.02` = c(455, 
917, 592, 679, 1391), `Unemployment Rate.02` = c(6, 8.8, 8.4, 
8.2, 5.4), `Labor Force.03` = c(7494, 10407, 7086, 8138, 25900
), Employed.03 = c(7059, 9418, 6485, 7475, 24702), `Unemployment Level.03` = c(435, 
989, 601, 663, 1198), `Unemployment Rate.03` = c(5.8, 9.5, 8.5, 
8.1, 4.6), `Labor Force.04` = c(7548, 10337, 7049, 8027, 26208
), Employed.04 = c(7122, 9580, 6516, 7416, 25101), `Unemployment Level.04` = c(426, 
757, 533, 611, 1107), `Unemployment Rate.04` = c(5.6, 7.3, 7.6, 
7.6, 4.2), `Labor Force.05` = c(7469, 10365, 7053, 7960, 26446
), Employed.05 = c(7146, 9840, 6669, 7527, 25491), `Unemployment Level.05` = c(323, 
525, 384, 433, 955), `Unemployment Rate.05` = c(4.3, 5.1, 5.4, 
5.4, 3.6), `Labor Force.06` = c(7506, 10591, 7170, 8113, 26770
), Employed.06 = c(7204, 10067, 6792, 7703, 25902), `Unemployment Level.06` = c(302, 
524, 378, 410, 868), `Unemployment Rate.06` = c(4, 4.9, 5.3, 
5.1, 3.2), `Labor Force.07` = c(7540, 10287, 6976, 8015, 26629
), Employed.07 = c(7135, 9712, 6602, 7613, 25780), `Unemployment Level.07` = c(405, 
575, 374, 402, 849), `Unemployment Rate.07` = c(5.4, 5.6, 5.4, 
5, 3.2), `Labor Force.08` = c(7376, 9984, 6991, 7925, 26698), 
    Employed.08 = c(6807, 9120, 6473, 7359, 25453), `Unemployment Level.08` = c(569, 
    864, 518, 566, 1245), `Unemployment Rate.08` = c(7.7, 8.7, 
    7.4, 7.1, 4.7), `Labor Force.09` = c(7132, 9519, 6869, 8042, 
    26480), Employed.09 = c(6334, 7832, 5890, 6942, 23832), `Unemployment Level.09` = c(798, 
    1687, 979, 1100, 2648), `Unemployment Rate.09` = c(11.2, 
    17.7, 14.3, 13.7, 10), `Labor Force.10` = c(7259, 9883, 6445, 
    7699, 24906), Employed.10 = c(6469, 8304, 5473, 6687, 22460
    ), `Unemployment Level.10` = c(790, 1579, 972, 1012, 2446
    ), `Unemployment Rate.10` = c(10.9, 16, 15.1, 13.1, 9.8), 
    `Labor Force.11` = c(7270, 9819, 6296, 7570, 25123), Employed.11 = c(6563, 
    8492, 5426, 6663, 22939), `Unemployment Level.11` = c(707, 
    1327, 870, 907, 2184), `Unemployment Rate.11` = c(9.7, 13.5, 
    13.8, 12, 8.7), `Labor Force.12` = c(7030, 9607, 6212, 7467, 
    24960), Employed.12 = c(6447, 8647, 5499, 6709, 23244), `Unemployment Level.12` = c(583, 
    960, 713, 758, 1716), `Unemployment Rate.12` = c(8.3, 10, 
    11.5, 10.2, 6.9), `Labor Force.13` = c(6954, 9644, 6208, 
    7420, 24887), Employed.13 = c(6421, 8737, 5486, 6727, 23325
    ), `Unemployment Level.13` = c(533, 907, 722, 693, 1562), 
    `Unemployment Rate.13` = c(7.7, 9.4, 11.6, 9.3, 6.3), `Labor Force.14` = c(6775, 
    9630, 6032, 7760, 24527), Employed.14 = c(6267, 8802, 5436, 
    7124, 23023), `Unemployment Level.14` = c(508, 828, 596, 
    636, 1504), `Unemployment Rate.14` = c(7.5, 8.6, 9.9, 8.2, 
    6.1), `Labor Force.15` = c(6718, 9379, 6050, 8011, 24485), 
    Employed.15 = c(6262, 8673, 5580, 7469, 23163), `Unemployment Level.15` = c(456, 
    706, 470, 542, 1322), `Unemployment Rate.15` = c(6.8, 7.5, 
    7.8, 6.8, 5.4), `Labor Force.16` = c(6733, 9548, 5991, 7779, 
    24623), Employed.16 = c(6295, 8872, 5534, 7246, 23298), `Unemployment Level.16` = c(438, 
    676, 457, 533, 1325), `Unemployment Rate.16` = c(6.5, 7.1, 
    7.6, 6.9, 5.4), `Labor Force.17` = c(6713, 9693, 5945, 7718, 
    24725), Employed.17 = c(6385, 9203, 5590, 7302, 23726), `Unemployment Level.17` = c(328, 
    490, 355, 416, 999), `Unemployment Rate.17` = c(4.9, 5.1, 
    6, 5.4, 4), `Labor Force.90` = c(6867, 10482, 6204, 8606, 
    19168), Employed.90 = c(6539, 9241, 5725, 7818, 17955), `Unemployment Level.90` = c(328, 
    1241, 479, 788, 1213), `Unemployment Rate.90` = c(4.8, 11.8, 
    7.7, 9.2, 6.3), `Labor Force.91` = c(6648, 10435, 6111, 8775, 
    19132), Employed.91 = c(6106, 9113, 5547, 7906, 18021), `Unemployment Level.91` = c(542, 
    1322, 564, 869, 1111), `Unemployment Rate.91` = c(8.2, 12.7, 
    9.2, 9.9, 5.8), `Labor Force.92` = c(6762, 10927, 6425, 9052, 
    19553), Employed.92 = c(6231, 9895, 5762, 8061, 18334), `Unemployment Level.92` = c(531, 
    1032, 663, 991, 1219), `Unemployment Rate.92` = c(7.9, 9.4, 
    10.3, 10.9, 6.2), `Labor Force.93` = c(6553, 11880, 6972, 
    9171, 19929), Employed.93 = c(5905, 10978, 6284, 8110, 18868
    ), `Unemployment Level.93` = c(648, 902, 688, 1061, 1061), 
    `Unemployment Rate.93` = c(9.9, 7.6, 9.9, 11.6, 5.3), `Labor Force.94` = c(6395, 
    12306, 7192, 9006, 20263), Employed.94 = c(5911, 11567, 6505, 
    8237, 19408), `Unemployment Level.94` = c(484, 739, 687, 
    769, 855), `Unemployment Rate.94` = c(7.6, 6, 9.6, 8.5, 4.2
    ), `Labor Force.95` = c(6472, 12777, 7129, 8938, 20993), 
    Employed.95 = c(6018, 11981, 6453, 8140, 20055), `Unemployment Level.95` = c(454, 
    796, 676, 798, 938), `Unemployment Rate.95` = c(7, 6.2, 9.5, 
    8.9, 4.5), `Labor Force.96` = c(6440, 13038, 7136, 8877, 
    21631), Employed.96 = c(6004, 12211, 6549, 8041, 20918), 
    `Unemployment Level.96` = c(436, 827, 587, 836, 713), `Unemployment Rate.96` = c(6.8, 
    6.3, 8.2, 9.4, 3.3), `Labor Force.97` = c(6456, 13175, 7392, 
    8824, 22684), Employed.97 = c(6087, 12175, 6823, 8131, 22003
    ), `Unemployment Level.97` = c(369, 1000, 569, 693, 681), 
    `Unemployment Rate.97` = c(5.7, 7.6, 7.7, 7.9, 3), `Labor Force.98` = c(6412, 
    12661, 7368, 8699, 23611), Employed.98 = c(6087, 11934, 6811, 
    8002, 22839), `Unemployment Level.98` = c(325, 727, 557, 
    697, 772), `Unemployment Rate.98` = c(5.1, 5.7, 7.6, 8, 3.3
    ), `Labor Force.99` = c(6428, 12410, 7016, 8577, 23968), 
    Employed.99 = c(6093, 11623, 6488, 7683, 23297), `Unemployment Level.99` = c(335, 
    787, 528, 894, 671), `Unemployment Rate.99` = c(5.2, 6.3, 
    7.5, 10.4, 2.8), `Labor Force.18` = c(6766, 9781, 5991, 7805, 
    25006), `Unemployment Rate.18` = c(6466, 9371, 5686, 7449, 
    24128), `Unemployment Level` = c(300, 410, 305, 356, 878), 
    `Unemployment Rate` = c(4.4, 4.2, 5.1, 4.6, 3.5)), row.names = c(NA, 
5L), class = "data.frame")

1 Ответ

1 голос
/ 09 февраля 2020

Сложность состоит в том, что год в именах столбцов кодируется по-разному, в некоторых столбцах используется год 4 di git, в некоторых столбцах - год 2 di git. (Кроме того, имена последних 3 столбцов subset, похоже, вообще не написаны.)

Проблема именования года может быть решена путем преобразования всех переменных в длинный формат, отделения и завершения года от имена переменных с последующим преобразованием в требуемый формат: 

library(data.table)
library(magrittr)
melt(setDT(subset), 
     id.vars = c("FIPS State", "Place Code", "Name", "longitude", "latitude", 
                 "COUNTY", "closest_metro"),
     measure.vars = patterns("\\d\\d$")) %>% 
  .[, c("variable", "Year") := tstrsplit(variable, "(?<=density)|\\.", perl = TRUE)] %>% 
  .[, Year := lubridate::ymd(Year, truncated = 2L) %>% year()] %>% 
  dcast(... ~ variable)

     FIPS State Place Code           Name longitude latitude        COUNTY closest_metro Year Employed Labor Force
  1:          1        124 Abbeville city -85.25130 31.56795  Henry County      24.33618 1990     6539        6867
  2:          1        124 Abbeville city -85.25130 31.56795  Henry County      24.33618 1991     6106        6648
  3:          1        124 Abbeville city -85.25130 31.56795  Henry County      24.33618 1992     6231        6762
  4:          1        124 Abbeville city -85.25130 31.56795  Henry County      24.33618 1993     5905        6553
  5:          1        124 Abbeville city -85.25130 31.56795  Henry County      24.33618 1994     5911        6395
 ---                                                                                                              
141:          1       1396   Allgood town -86.51611 33.90762 Blount County      30.29652 2014    23023       24527
142:          1       1396   Allgood town -86.51611 33.90762 Blount County      30.29652 2015    23163       24485
143:          1       1396   Allgood town -86.51611 33.90762 Blount County      30.29652 2016    23298       24623
144:          1       1396   Allgood town -86.51611 33.90762 Blount County      30.29652 2017    23726       24725
145:          1       1396   Allgood town -86.51611 33.90762 Blount County      30.29652 2018       NA       25006
     Unemployment Level Unemployment Rate      density
  1:                328               4.8 7.859482e-05
  2:                542               8.2 7.963680e-05
  3:                531               7.9 7.896695e-05
  4:                648               9.9 7.894215e-05
  5:                484               7.6 7.871886e-05
 ---                                                  
141:               1504               6.1 2.312020e-04
142:               1322               5.4 2.308303e-04
143:               1325               5.4 2.308303e-04
144:                999               4.0 2.312020e-04
145:                 NA           24128.0 2.312020e-04

Исправление имен столбцов для 2018

Измененные данные показывают некоторые значения для 2018 до появляются в неправильных столбцах или отсутствуют вообще. При проверке subset кажется, что последние 3 столбца получили неправильные имена столбцов. Это можно исправить, переименовав столбцы с помощью функции data.tables set_names(): 

library(data.table)
library(magrittr)
setDT(subset) %>% 
  setnames(c("Unemployment Rate.18", "Unemployment Level", "Unemployment Rate"),
           c("Employed.18", "Unemployment Level.18", "Unemployment Rate.18")) %>% 
  melt(id.vars = c("FIPS State", "Place Code", "Name", "longitude", "latitude", 
                   "COUNTY", "closest_metro"),
       measure.vars = patterns("\\d\\d$")) %>% 
  .[, c("variable", "Year") := tstrsplit(variable, "(?<=density)|\\.", perl = TRUE)] %>% 
  .[, Year := lubridate::ymd(Year, truncated = 2L) %>% lubridate::year()] %>% 
  dcast(... ~ variable)

     FIPS State Place Code           Name longitude latitude        COUNTY closest_metro Year Employed Labor Force
  1:          1        124 Abbeville city -85.25130 31.56795  Henry County      24.33618 1990     6539        6867
  2:          1        124 Abbeville city -85.25130 31.56795  Henry County      24.33618 1991     6106        6648
  3:          1        124 Abbeville city -85.25130 31.56795  Henry County      24.33618 1992     6231        6762
  4:          1        124 Abbeville city -85.25130 31.56795  Henry County      24.33618 1993     5905        6553
  5:          1        124 Abbeville city -85.25130 31.56795  Henry County      24.33618 1994     5911        6395
 ---                                                                                                              
141:          1       1396   Allgood town -86.51611 33.90762 Blount County      30.29652 2014    23023       24527
142:          1       1396   Allgood town -86.51611 33.90762 Blount County      30.29652 2015    23163       24485
143:          1       1396   Allgood town -86.51611 33.90762 Blount County      30.29652 2016    23298       24623
144:          1       1396   Allgood town -86.51611 33.90762 Blount County      30.29652 2017    23726       24725
145:          1       1396   Allgood town -86.51611 33.90762 Blount County      30.29652 2018    24128       25006
     Unemployment Level Unemployment Rate      density
  1:                328               4.8 7.859482e-05
  2:                542               8.2 7.963680e-05
  3:                531               7.9 7.896695e-05
  4:                648               9.9 7.894215e-05
  5:                484               7.6 7.871886e-05
 ---                                                  
141:               1504               6.1 2.312020e-04
142:               1322               5.4 2.308303e-04
143:               1325               5.4 2.308303e-04
144:                999               4.0 2.312020e-04
145:                878               3.5 2.312020e-04
...